Emanuela Raffinetti

Papers

Total Citations

225

H-Index

About

Emanuela Raffinetti is a prominent researcher at the intersection of artificial intelligence, explainability, and financial applications, whose work has rapidly gained significant traction in the academic community. Her research focuses on developing frameworks for safe and transparent AI systems within financial contexts, addressing one of the most pressing challenges facing modern quantitative finance: ensuring that complex machine learning models remain interpretable, accountable, and trustworthy. Raffinetti's most influential contribution is her SAFE Artificial Intelligence in Finance framework, which has garnered an impressive 142 citations since its 2023 publication, reflecting the urgent demand for principled guidelines governing AI deployment in high-stakes financial environments. Complementing this foundational work, her 2022 study on explainable artificial intelligence for crypto asset allocation — accumulating 74 citations — demonstrates her ability to apply cutting-edge interpretability methodologies to emerging digital asset markets, providing investors and analysts with more transparent decision-support tools. Through her scholarship, Raffinetti has positioned herself as a leading voice advocating for responsible AI adoption in finance, bridging technical rigor with practical relevance. Her rapidly growing citation record signals that her frameworks are already shaping how researchers and practitioners think about ethical and explainable AI in an increasingly algorithm-driven financial landscape.